Pose Classification

Pose classification using ST-GCN (Spatial Temporal Graph Convolutional Network). Classifies skeleton sequences into action categories from pose keypoint data.

Typically trained from scratch on skeleton data.

Dataclass Schemas

Generated TAO Core schemas are packaged in

schemas/<action>.schema.json

, with

schemas/manifest.json

listing available actions. Each generated schema also emits

references/spec_template_<action>.yaml

from the schema top-level

default

field. AutoML enablement is declared at the model layer in

references/skill_info.yaml

via

automl_enabled

. Runnable AutoML still requires

schemas/train.schema.json

and

references/spec_template_train.yaml

to exist and parse. Use the packaged train schema for

automl_default_parameters

automl_disabled_parameters

, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect

~/tao-core

at runtime; maintainers regenerate schemas/templates before packaging the skill bank.

Train Action Policy

This model is AutoML-enabled at the model layer. Before handling any train-stage request, read

references/skill_info.yaml

and resolve the run override from either an explicit

automl_policy

value or the user's workflow request. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as

automl_policy: off

for this run only; otherwise default to

auto

. When

automl_policy: auto

automl_enabled: true

, and both

schemas/train.schema.json

and

references/spec_template_train.yaml

are packaged, route the train action through

tao-skill-bank:tao-run-automl

by default with this model's

skill_dir

. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and

automl_policy

. Use direct model training only when

automl_policy: off

or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.

Non-train actions such as

evaluate

inference

export

, and deploy flows stay in this model skill. The per-run

automl_policy

override does not change model metadata.

Training Requirements

Dataset type: pose_classification
Formats: default
Monitoring metric: val_acc

Per-Action Dataset Requirements

Action	Spec Key	Source	List?
evaluate	evaluate.test_dataset.data_path	train_datasets	No
evaluate	evaluate.test_dataset.label_path	train_datasets	No
inference	inference.test_dataset.data_path	train_datasets	No
train	dataset.train_dataset.data_path	train_datasets	No
train	dataset.train_dataset.label_path	train_datasets	No
train	dataset.val_dataset.data_path	train_datasets	No
train	dataset.val_dataset.label_path	train_datasets	No

Typical Spec Overrides

Data source overrides are mandatory for every action — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in

spec_overrides

python

S3_TRAIN = "s3://bucket/data/train"

train (mandatory data sources):

python

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "num_classes": 6,
    "graph_layout": "nvidia",
    "dataset.train_dataset.data_path": f"{S3_TRAIN}",
    "dataset.train_dataset.label_path": f"{S3_TRAIN}",
    "dataset.val_dataset.data_path": f"{S3_TRAIN}",
    "dataset.val_dataset.label_path": f"{S3_TRAIN}",
}

evaluate (mandatory data sources):

python

{
    "evaluate.test_dataset.data_path": f"{S3_TRAIN}",
    "evaluate.test_dataset.label_path": f"{S3_TRAIN}",
}

inference (mandatory data sources):

python

{
    "inference.test_dataset.data_path": f"{S3_TRAIN}",
}

Eval Dataset

Optional. Validation data is provided alongside training as val_data.npy / val_label.pkl.

Important Parameters

dataset.num_classes: Number of pose action classes. Default 6.
model.graph_layout: Skeleton graph layout. Options: nvidia, openpose. Determines joint connectivity.
model.graph_strategy: Graph partitioning strategy for GCN.
train.optim.lr: Learning rate. Default 0.1 (SGD). Higher than vision models due to graph convolution properties.
model.dropout: Dropout rate for regularization.

Multi-GPU / Multi-Node

Launch method: Lightning-managed (single

python

process, Lightning spawns workers).

Spec Key	Description	Default
`train.num_gpus`	Number of GPUs	1
`train.gpu_ids`	GPU device indices	[0]

Strategy:
```
auto
```
(Lightning picks best strategy automatically)
No explicit
```
num_nodes
```
or
```
distributed_strategy
```
config — single-node only
Lightweight model, single GPU typically sufficient

Hardware

Minimum 1 GPU(s), recommended 1 GPU(s). 8GB+ VRAM per GPU. Pose classification is very lightweight — skeleton data is small. Single GPU is sufficient.

Error Patterns

Graph layout mismatch: Ensure model.graph_layout matches the skeleton format in your .npy data files.

Label shape mismatch: train_label.pkl class indices must be in range [0, num_classes).

Spec Param / Parent Model Inference

Model-specific inference mappings belong in this MD file, not in

config.json

. Generated runners should read this section and apply the mappings with SDK helpers before

create_job()

. This mirrors the old microservices

infer_params.py

flow.

Inference mappings from TAO Core

pose_classification.config.json

Action	Spec Field	Inference Function	Meaning
evaluate	`encryption_key`	`key`	encryption key
evaluate	`evaluate.checkpoint`	`parent_model`	model file inferred from the parent job results folder
evaluate	`results_dir`	`output_dir`	current job results directory
export	`encryption_key`	`key`	encryption key
export	`export.checkpoint`	`parent_model`	model file inferred from the parent job results folder
export	`export.onnx_file`	`create_onnx_file`	output ONNX path
export	`results_dir`	`output_dir`	current job results directory
inference	`encryption_key`	`key`	encryption key
inference	`inference.checkpoint`	`parent_model`	model file inferred from the parent job results folder
inference	`inference.output_file`	`create_inference_result_file_pose`	pose inference result file
inference	`results_dir`	`output_dir`	current job results directory
train	`encryption_key`	`key`	encryption key
train	`model.pretrained_model_path`	`ptm_if_no_resume_model`	PTM when no resume checkpoint exists
train	`results_dir`	`output_dir`	current job results directory
train	`train.resume_training_checkpoint_path`	`resume_model`	model file inferred from the current job results folder

For

parent_model

parent_model_folder

, pass the upstream train/export/AutoML child job id as

parent_job_id

. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to

config.json

and do not patch generated runner scripts to guess checkpoint paths.

tao-train-pose-classification

NPX Install

Tags

SKILL.md Content

Pose Classification

Dataclass Schemas

Train Action Policy

Training Requirements

Per-Action Dataset Requirements

Typical Spec Overrides

Eval Dataset

Important Parameters

Multi-GPU / Multi-Node

Hardware

Error Patterns

Spec Param / Parent Model Inference